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Proceedings Paper

Reconstructing Irregularly Sampled Images by Neural Networks
Author(s): Albert J. Ahumada; John I. Yellott
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Paper Abstract

Neural-network-like models of receptor position learning and interpolation function learning are being developed as models of how the human nervous system might handle the problems of keeping track of the receptor positions and interpolating the image between receptors. These models may also be of interest to designers of image processing systems desiring the advantages of a retina-like image sampling array.

Paper Details

Date Published: 15 August 1989
PDF: 8 pages
Proc. SPIE 1077, Human Vision, Visual Processing, and Digital Display, (15 August 1989); doi: 10.1117/12.952721
Show Author Affiliations
Albert J. Ahumada, NASA Ames Research Center (United States)
John I. Yellott, University of California (United States)


Published in SPIE Proceedings Vol. 1077:
Human Vision, Visual Processing, and Digital Display
Bernice E. Rogowitz, Editor(s)

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